# Localizing and Quantifying Damage in Social Media Images

arXiv: https://arxiv.org/abs/1806.07378 · HTML: https://mlsanity.com/paper/1806.07378/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2018.

## Built on by (most cited first, 7 of 7)

- [MEDIC: A Multi-Task Learning Dataset for Disaster Image Classification](https://mlsanity.com/paper/2108.12828/) (2021)
- [Robust Training of Social Media Image Classification Models for Rapid Disaster Response](https://mlsanity.com/paper/2104.04184/) (2021)
- [Post-hurricane building damage assessment using street-view imagery and structured data: A multi-modal deep learning approach](https://mlsanity.com/paper/2404.07399/) (2024)
- [Deep-Disaster: Unsupervised Disaster Detection and Localization Using Visual Data](https://mlsanity.com/paper/2202.00050/) (2022)
- [A multi-modal approach towards mining social media data during natural disasters -- a case study of Hurricane Irma](https://mlsanity.com/paper/2101.00480/) (2021)
- [From Pixels to Damage Severity: Estimating Earthquake Impacts Using Semantic Segmentation of Social Media Images](https://mlsanity.com/paper/2507.02781/) (2025)
- [Information Cascade Prediction under Public Emergencies: A Survey](https://mlsanity.com/paper/2404.01319/) (2024)

## Abstract (arXiv)

Traditional post-disaster assessment of damage heavily relies on expensive GIS data, especially remote sensing image data. In recent years, social media has become a rich source of disaster information that may be useful in assessing damage at a lower cost. Such information includes text (e.g., tweets) or images posted by eyewitnesses of a disaster. Most of the existing research explores the use of text in identifying situational awareness information useful for disaster response teams. The use of social media images to assess disaster damage is limited. In this paper, we propose a novel approach, based on convolutional neural networks and class activation maps, to locate damage in a disaster image and to quantify the degree of the damage. Our proposed approach enables the use of social network images for post-disaster damage assessment and provides an inexpensive and feasible alternative to the more expensive GIS approach.